US20210081587A1 - Method for building predictive model of microorganism-derived dissolved organic nitrogen in wastewater - Google Patents

Method for building predictive model of microorganism-derived dissolved organic nitrogen in wastewater Download PDF

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US20210081587A1
US20210081587A1 US17/024,656 US202017024656A US2021081587A1 US 20210081587 A1 US20210081587 A1 US 20210081587A1 US 202017024656 A US202017024656 A US 202017024656A US 2021081587 A1 US2021081587 A1 US 2021081587A1
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mdon
don
model
nitrogen
bacteria
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Haidong HU
Kewei LIAO
Hongqiang Ren
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Nanjing University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/006—Regulation methods for biological treatment
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/34—Biological treatment of water, waste water, or sewage characterised by the microorganisms used
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G16B5/20—Probabilistic models
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/44—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2101/00—Nature of the contaminant
    • C02F2101/30—Organic compounds
    • C02F2101/38—Organic compounds containing nitrogen
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/08—Chemical Oxygen Demand [COD]; Biological Oxygen Demand [BOD]
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/14—NH3-N
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/15—N03-N
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/16—Total nitrogen (tkN-N)
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/22—O2
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling

Definitions

  • the disclosure relates to the field of wastewater treatment, and more particularly, to a method for building a predictive model of microorganism-derived dissolved organic nitrogen (mDON) in wastewater and to application thereof.
  • mDON microorganism-derived dissolved organic nitrogen
  • the effluent of the municipal wastewater treatment plants includes dissolved organic nitrogen (DON).
  • DON dissolved organic nitrogen
  • the DON includes influent-derived dissolved organic nitrogen (inDON) which is non-degradable and microorganism-derived dissolved organic nitrogen produced in the biological sewage treatment process.
  • inDON influent-derived dissolved organic nitrogen
  • mDON produced in the wastewater treatment process is more easily affected by the process parameters and conditions, and the composition and properties of mDON are closely related to the growth and metabolism of microorganisms in the biological treatment process.
  • the disclosure provides a method for building a predictive model of microorganism-derived dissolved organic nitrogen (mDON) in wastewater. Specifically, based on the operating parameters of an activated sludge process, the component concentrations, and the kinetic and stoichiometric parameters of the influent of a sewage plant, an activated sludge model (ASM)-mDON predictive model is built.
  • ASM activated sludge model
  • a method for building a predictive model of mDON in wastewater comprising:
  • the activated sludge system comprises a fully mixed steady state activated sludge; the activated sludge has a sludge age of 5-30 days, and a concentration of 2000-5000 mg/L.
  • the initial values of the parameter variables are determined with reference to “Mathematical Model for Activated Sludge”.
  • the kinetic and stoichiometric parameters of the ASM-mDON model are classified for parameter assumption, parameter estimation, or default argument assignment.
  • the sensitivity analysis uses the absolute-relative sensitivity equation to determine the influence of different values of an independent parameter on the estimation of the mDON.
  • the ASM-mDON model is used for study of the mDON released by microorganisms in the activated sludge system, and the model comprises:
  • X H ⁇ : ⁇ ⁇ dX H dt ⁇ ⁇ H ⁇ M H , NH ⁇ ( t ) ⁇ M H , O ⁇ ( t ) ⁇ X H ⁇ ( t ) - b H ⁇ M H , O ⁇ ( t ) ⁇ X H ⁇ ( t ) ( 1 )
  • X A ⁇ : ⁇ ⁇ dX A dt ⁇ ⁇ A ⁇ M A , NH ⁇ ( t ) ⁇ M A , O ⁇ ( t ) ⁇ X A ⁇ ( t ) - b A ⁇ M A , O ⁇ ( t ) ⁇ X A ⁇ ( t ) ( 2 )
  • S NH ⁇ : ⁇ ⁇ dS NH dt - ( f H , DON Y H + i XB ) ⁇ ⁇ ⁇ H ⁇ M H
  • M H,NH (t) is a Monod term determined by the substrate for the heterotrophic bacteria
  • M A,NH (t) is a Monod term determined by the substrate for the autotrophic bacteria
  • M H,O (t) is a Monod term determined by the dissolved oxygen for the heterotrophic bacteria
  • M A,O (t) is a Monod term determined by the dissolved oxygen for the autotrophic bacteria
  • M H,DON (t) is a Monod term determined by the mDON in the heterotrophic bacteria
  • k L ⁇ is an exchange rate between the gas phase and the liquid phase
  • S O * is the maximum solubility of oxygen.
  • the mDON in the wastewater is calculated using the following kinetic equation:
  • dS DON dt f H , DON Y H ⁇ ⁇ ⁇ H ⁇ M H , NH ⁇ ( t ) ⁇ M H , O ⁇ ( t ) ⁇ X H ⁇ ( t ) + f A , DON Y A ⁇ ⁇ ⁇ A ⁇ M A , NH ⁇ ( t ) ⁇ M A , O ⁇ ( t ) ⁇ X A ⁇ ( t ) - k a ⁇ M H , DON ⁇ ( t ) ⁇ X H ⁇ ( t ) ( 8 )
  • the single-step size of the AMS-mDON model is 0.1, and the total response time for the predictive model is the product of the calculation capacity and the single-step size.
  • the disclosure also provides a method for predicting a concentration of mDON in wastewater, the method comprising:
  • the wastewater treatment plant operates at the ambient temperature ranging from 15 to 25° C., and an influent pH thereof is 6.0-8.0.
  • the membrane filter is a cellulose acetate membrane filter having pore size of 0.45 ⁇ m.
  • the initial values of parameter variables are determined with reference to “Mathematical Model of Activated Sludge”.
  • the kinetic and stoichiometric parameters of the ASM-mDON model are classified for parameter assumption, parameter estimation, or default argument assignment.
  • the concentration of the dissolved organic nitrogen is the difference between the total nitrogen and ammonia nitrogen, nitrate nitrogen and nitrite nitrogen; the concentration of total nitrogen is measured by using potassium persulfate oxidation-ion chromatography, or potassium persulfate oxidation-ultraviolet spectrophotometry; the concentration of ammonia nitrogen is measured by using salicylic acid-hypochlorite spectrophotometry; the nitrate nitrogen is measured by using the ion chromatography or ultraviolet-visible spectrophotometry; the nitrite nitrogen is measured by using ion chromatography or N-(1-naphthyl)-ethylenediamine spectrophotometry; and the COD is measured by using potassium dichromate method or rapid digestion method.
  • the ASM-mDON model can predict the concentration of the mDON in the wastewater, distinguish the mDON from the inDON, and quantify the concentration of the mDON released from the activated sludge in the wastewater treatment plant.
  • the method uses a simplified ASM model, and necessary kinetic and stoichiometric parameters to build an ASM-mDON model for predicting the concentration of the mDON in the wastewater, which simplifies the operations and improves the prediction accuracy.
  • the method can be widely applied in simulating and predicting the concentration of the mDON, laying the foundation for optimization of water quality in the wastewater treatment plants.
  • FIG. 1 is a flow chart of a method for building a predictive model of mDON according to one embodiment of the disclosure
  • FIG. 2 is a graft showing the predictive result of the concentration of the mDON according to one embodiment of the disclosure.
  • FIG. 3 is a graft showing the predictive result of the concentration of the mDON according to verification embodiment of the disclosure.
  • the example was a simulation of the operation of a laboratory-scale sequencing batch reactor (SBR) for the treatment of activated sludge.
  • SBR laboratory-scale sequencing batch reactor
  • the wastewater containing particular compositions (excluding dissolved organic nitrogen) was prepared to support the growth of microorganisms in the activated sludge.
  • the prepared wastewater contained the following compositions: 300 ⁇ 30 mg/L COD, 20 ⁇ 5 mg/L total nitrogen, and 3.5 ⁇ 0.5 mg/L total phosphorus.
  • the operating parameters of the sequencing batch reactor the effective volume of 2 L, the operating cycle of 6 h, and the hydraulic retention time of 12 h, and the activated sludge age of 20 d.
  • a method for building a predictive model of mDON in wastewater comprises:
  • the inputs to the ASM-mDON model comprises: X H (concentration of heterotrophic bacteria), X A (concentration of autotrophic bacteria), X I (concentration of inert particles), S NO (concentration of nitrate nitrogen), S NH (concentration of ammonia nitrogen), S DON (concentration of the mDON), S O (concentration of dissolved oxygen), which were state variables; the model matrix corresponding to the process rate equation for the components were inputted into the reaction process included in the software, thereby building a simulation of the sequencing batch reactor for the treatment of activated sludge.
  • the simulation model was the simplified ASM-mDON model as shown in Table 1:
  • the concentration of total nitrogen was 20 mg/L measured by using potassium persulfate oxidation-ion chromatography, or potassium persulfate oxidation-ultraviolet spectrophotometry; the concentration of ammonia nitrogen was 20 mg/L measured by using salicylic acid-hypochlorite spectrophotometry; the nitrate nitrogen was 0 mg/L measured by using the ion chromatography or ultraviolet-visible spectrophotometry; the nitrite nitrogen was 0 mg/L measured by using ion chromatography or N-(1-naphthyl)-ethylenediamine spectrophotometry; and the COD was 300 mg/L measured by using potassium dichromate method or rapid digestion method. The concentration of the dissolved organic nitrogen was 0 mg/L, that is, the difference between the sum of total nitrogen and ammoni
  • the default values for kinetics and stoichiometric parameters of the conventional model, and the water quality parameters determined in 1) were used for simulation of parameters in relation to the mDON yielded in the wastewater treatment process; the simulation parameters were set as follows: ⁇ circumflex over ( ⁇ ) ⁇ H , 0.8 h ⁇ 1 , Y H , 0.67 mg (COD)/mg (N); b H , 0.62 h ⁇ 1 ; K H,NH , 0.05 mg (N)/L; K H,O , 0.2 mg (N)/L; ⁇ circumflex over ( ⁇ ) ⁇ A , 0.3 h ⁇ 1 ; f H,DON , 0.04; Y A , 3.4 mg (COD)/mg (N); b A , 0.15 h ⁇ 1 ; K A,NH , 5 mg (N)/L; K A,O , 0.4 mg (N)/L; f A,DON , 0.04, i XB
  • f NO was the substrate utilization ratio of autotrophic bacteria converting the substrates into the nitrate nitrogen
  • f I was the rate of inert particles yielded in the organism
  • k a was the ammonification rate
  • K H,DON was the half-saturation constant for mDON.
  • the components of influent and values of model parameters determined in 2) were fed into the software for modeling mDON to predict the concentration of the mDON in wastewater; where the single-step size was 0.1, and the calculation capacity was 60 steps, and the simulation process was based on the mDON participating in the biochemical reactions.
  • the model-predicted result was shown in FIG. 2 .
  • the example was the same as Example 1, except for the influent from the municipal wastewater treatment plant A.
  • the operating parameters of the sequencing batch reactor the influent temperature was 15° C., and hydraulic retention time was 8 h, activated sludge age was 20 d.
  • the influent contains the following compositions: COD 96.2-120.6 mg/L, total nitrogen 23.7-29.1 mg/L, total phosphorus 2.0-3.5 mg/L, pH 7.4-8.0, and inert particles 3000-3200 mg/L.
  • the influent (of greater than 200 mL) in the biological treatment process (i.e. oxidation ditch) and the activated sludge (of greater than 50 mL) were sampled for analysis of the components of the influent, as well as parameter estimation.
  • the influent sample was then filtered using a cellulose acetate membrane filter having pore size of 0.45 ⁇ m.
  • the COD was measured by using potassium dichromate method or rapid digestion method; the concentration of total nitrogen was measured by using potassium persulfate oxidation-ion chromatography, or potassium persulfate oxidation-ultraviolet spectrophotometry; the concentration of ammonia nitrogen was measured by using salicylic acid-hypochlorite spectrophotometry; the nitrate nitrogen was measured by using the ion chromatography or ultraviolet-visible spectrophotometry; the nitrite nitrogen was measured by using ion chromatography or N-(1-naphthyl)-ethylenediamine spectrophotometry; the concentration of dissolved organic nitrogen was the difference between the sum of total nitrogen and ammonia nitrogen and the sum of nitrate nitrogen and nitrite nitrogen.
  • the initial values of the yield coefficient (Y H ) of heterotrophic bacteria, the attenuation coefficient of heterotrophic bacteria, and the maximum specific growth rate ( ⁇ circumflex over ( ⁇ ) ⁇ H ) of heterotrophic bacteria were 0.26 mgCOD/mgN, 0.09 h ⁇ 1 , and 1.0 h ⁇ 1 , respectively.
  • Predicting the concentration of the mDON in wastewater the components of influent and values of model parameters determined in 2) were fed into the software for modeling mDON to predict the concentration of the mDON in wastewater; where the single-step size was 0.1, and the calculation capacity was 240 steps.
  • the model-predicted concentration of the mDON yielded in the oxidation ditch was 2.32 mg/L.
  • the example was the same as Example 2, except for the influent coming from the municipal wastewater treatment plant A was sampled at different times.
  • the operating parameters of the sequencing batch reactor the influent temperature was 20° C., and hydraulic retention time was 8 h, activated sludge age was 5 d.
  • the influent contains the following compositions: COD 96.2-120.6 mg/L, total nitrogen 23.7-29.1 mg/L, total phosphorus 2.0-3.5 mg/L, pH 7.4-8.0, and inert particles 3000-3200 mg/L.
  • the influent (of greater than 200 mL) in the biological treatment process (i.e. oxidation ditch) and the activated sludge (of greater than 50 mL) were sampled for analysis of the components of the influent and parameter estimation.
  • the influent sample was then filtered using a cellulose acetate membrane filter having pore size of 0.45 ⁇ m.
  • COD was measured by using potassium dichromate method or rapid digestion method; the concentration of total nitrogen was measured by using potassium persulfate oxidation-ion chromatography, or potassium persulfate oxidation-ultraviolet spectrophotometry; the concentration of ammonia nitrogen was measured by using salicylic acid-hypochlorite spectrophotometry; the nitrate nitrogen was measured by using the ion chromatography or ultraviolet-visible spectrophotometry; the nitrite nitrogen was measured by using ion chromatography or N-(1-naphthyl)-ethylenediamine spectrophotometry; the concentration of dissolved organic nitrogen was the difference between the sum of total nitrogen and ammonia nitrogen and the sum of nitrate nitrogen and nitrite nitrogen.
  • the initial values of the yield coefficient (Y H ) of heterotrophic bacteria, the attenuation coefficient of heterotrophic bacteria, and the maximum specific growth rate (PH) of heterotrophic bacteria were 0.26 mgCOD/mgN, 0.09 h ⁇ 1 , and 1.0 h ⁇ 1 , respectively.
  • Predicting the concentration of mDON in wastewater the components of influent and values of model parameters determined in 2) were fed into the software for modeling mDON to predict the concentration of the mDON in wastewater; where the single-step size was 0.1, and the calculation capacity was 240 steps.
  • the model-predicted concentration of the mDON yielded in the oxidation ditch was 1.89 mg/L.
  • the example was the same as Example 1, except for the influent from the municipal wastewater treatment plant B.
  • the influent contained the following compositions: COD 130.9 mg/L, total nitrogen 25.1 mg/L, total phosphorus 5.1 mg/L, pH 7.2, and inert particles 3000-3200 mg/L.
  • the influent (of greater than 200 mL) in the biological treatment process (i.e. oxidation ditch) and the activated sludge (of greater than 50 mL) were sampled for analysis of the components of the influent and parameter estimation.
  • the influent sample was then filtered using a cellulose acetate membrane filter having pore size of 0.45 ⁇ m.
  • COD was measured by using potassium dichromate method or rapid digestion method; the concentration of total nitrogen was measured by using potassium persulfate oxidation-ion chromatography, or potassium persulfate oxidation-ultraviolet spectrophotometry; the concentration of ammonia nitrogen was measured by using salicylic acid-hypochlorite spectrophotometry; the nitrate nitrogen was measured by using the ion chromatography or ultraviolet-visible spectrophotometry; the nitrite nitrogen was measured by using ion chromatography or N-(1-naphthyl)-ethylenediamine spectrophotometry; the concentration of dissolved organic nitrogen was the difference between the sum of total nitrogen and ammonia nitrogen and the sum of nitrate nitrogen and nitrite nitrogen.
  • the initial values of the yield coefficient (Y H ) of heterotrophic bacteria, the attenuation coefficient of heterotrophic bacteria, and the maximum specific growth rate ( ⁇ circumflex over ( ⁇ ) ⁇ H ) of heterotrophic bacteria were 0.2 mgCOD/mgN, 0.05 h ⁇ 1 , and 0.3 h ⁇ 1 , respectively.
  • Predicting the concentration of mDON in wastewater the components of influent and values of model parameters determined in 2) were fed into the software for modeling mDON to predict the concentration of the mDON in wastewater; where the single-step size was 0.1, and the calculation capacity was 60 steps.
  • the model-predicted concentration of the mDON yielded in the oxidation ditch was 4.31 mg/L.
  • the influent entering the sequencing batch reactor in Example 1 was sampled to measure the concentration of the mDON that was then verified with the model-predicted concentration.
  • the operating cycle for the sequencing batch reactor was 5 h, under which the influent was sampled per 0.5 h interval.
  • the influent samples were filtered and used to undergo measurement with reference to the methods described in Example 1. Referring to FIG. 3 , the measured concentration of the mDON in the entire operating cycle was basically fitted to the model-predicted concentration, lying in the calculated error range.
  • the measured concentration of the mDON is close to the model-predicted concentration of the mDON yielded in the activated sludge process in accordance with the Verification Example of the disclosure.
  • the disclosure offers many advantages in simplicity, accuracy, and fast prediction over current methods, thereby being widely applied in prediction of the mDON yielded in activated sludge process.

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